%0 Report
%A Conde Ruiz, José Ignacio
%A Ganuza, Juan-José
%A García, Manu
%A Puch, Luis A.
%T Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach
%J Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
%D 2021
%@ 2341-2356
%U https://hdl.handle.net/20.500.14352/5765
%X We analyze all the articles published in the top five (T5) Economics journals be- tween 2002 and 2019 in order to find gender differences in their research approach. We implement an unsupervised machine learning algorithm: the Structural Topic Model (STM), so as to incorporate gender document-level meta-data into a probabilistic text model. This algorithm characterizes jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated to each latent topic. Latent topics are mixtures over words where each word has a probability of belonging to a topic after controlling by journal name and publication year (the meta-data). Thus, the topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that fe- males are unevenly distributed along the estimated latent topics, by using only data driven methods. This finding relies on “automatically” generated built-in data given the contents in the abstracts of the articles in the T5 journals, without any arbitrary allocation of texts to particular categories (as JEL codes, or research areas).
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